New biomarker may lead to new clinical laboratory testing and treatments for long COVID
Researchers studying long COVID at the University Hospital of Zurich (UZH) and the Swiss Institute of Bioinformatics (SIB), both in Switzerland, have discovered a protein biomarker in blood that indicates a component of the body’s innate immune system—called the complement system—remains active in some individuals long after the infection has run its course. The scientists are hopeful that further studies may provide clinical laboratories with a definitive test for long COVID, and pharma companies with a path to develop therapeutic drugs to treat it.
Ever since the COVID-19 pandemic began, a subset of the population worldwide continues to experience lingering symptoms even after the acute phase of the illness has passed. Patients with long COVID experience symptoms for weeks, even months after the initial viral infection has subsided. And because these symptoms can resemble other illnesses, long COVID is difficult to diagnose.
This new biomarker may lead to new clinical laboratory diagnostic blood tests for long COVID, and to a greater understanding of why long COVID affects some patients and not others.
“Those long COVID patients used to be like you and me, totally integrated [into] society with a job, social life, and private life,” infectious disease specialist Michelè van Vugt, MD (above), Senior Fellow and Professor at Amsterdam Institute for Global Health and Development (AIGHD), told Medical News Today. “After their COVID infection, for some of them, nothing was left because of their extreme fatigue. And this happened not only in one patient but many more—too many for only [a] psychological cause.” Clinical laboratories continue to perform tests on patients experiencing symptoms of COVID-19 even after the acute illness has passed. (Photo copyright: AIGHD.)
Role of the Complement System
To complete their study, the Swiss scientists monitored 113 patients who were confirmed through a reverse transcriptase quantitative polymerase chain reaction (RT-qPCR) test to have COVID-19. The study also included 39 healthy control patients who were not infected.
The researchers examined 6,596 proteins in 268 blood samples collected when the sick patients were at an acute stage of the virus, and then again six months after the infection. They found that 40 of the patients who were sick with COVID-19 eventually developed symptoms of long COVID. Those 40 patients all had a group of proteins in their blood showing that the complement system of their immune system was still elevated even after recovering from the virus.
“Complement is an arm of the immune system that ‘complements’ the action of the other arms,” Amesh Adalja, MD, Adjunct Assistant Professor at Johns Hopkins Bloomberg School of Public Health, told Prevention, “Activities that it performs range from literally attacking the cell membranes of a pathogen to summoning the cells of other immune systems to the site of infection.”
In addition to helping bodies heal from injury and illness, the complement immune system also activates inflammation in the body—and if the complement system is activated for too long the patient is at risk for autoimmune disease and other inflammatory conditions.
Conducted by genetic scientists at Trinity College Dublin and St. James’ Hospital in Dublin, Ireland, the study “analyzed blood samples—specifically, serum and plasma—from 76 patients who were hospitalized with COVID-19 in March or April 2020, along with those from 25 people taken before the pandemic. The researchers discovered that people who said they had brain fog had higher levels of a protein in their blood called S100β [a calcium-binding protein] than people who didn’t have brain fog,” Prevention reported.
“S100β is made by cells in the brain and isn’t normally found in the blood. That suggests that the patients had a breakdown in the blood-brain barrier, which blocks certain substances from getting to the brain and spinal cord, the researchers noted,” Prevention reported.
“The scientists then did MRI scans with dye of 22 people with long COVID (11 of them who reported having brain fog), along with 10 people who recovered from COVID-19. They found that long COVID patients who had brain fog had signs of a leaky blood-brain barrier,” Prevention noted.
“This leakiness likely disrupts the integrity of neurons in the brain by shifting the delicate balance of materials moving into and out of the brain,” Matthew Campbell, PhD, Professor and Head of Genetics at Trinity College Dublin, told Prevention.
Interactions with Other Viruses
According to Medical News Today, the Swiss study results also suggest that long COVID symptoms could appear because of the reactivation of a previous herpesvirus infection. The patients in the study showed increased antibodies against cytomegalovirus, a virus that half of all Americans have contracted by age 40.
The link between long COVID and these other viruses could be key to developing treatment for those suffering with both illnesses. The antiviral treatments used for the herpesvirus could potentially help treat long COVID symptoms as well, according to Medical News Today.
“Millions of people across the planet have long COVID or will develop it,” Thomas Russo MD, Professor and Chief of Infectious Disease at the University at Buffalo in New York, told Prevention. “It’s going to be the next major phase of this pandemic. If we don’t learn to diagnose and manage this, we are going to have many people with complications that impact their lives for the long term.”
Long COVID won’t be going away any time soon, much like the COVID-19 coronavirus. But these two studies may lead to more effective clinical laboratory testing, diagnoses, and treatments for millions of people suffering from the debilitating condition.
New artificial intelligence model agrees with interpretations of human medical technologists and microbiologists with extraordinary accuracy
Microbiology laboratories will be interested in news from Brescia University in Italy, where researchers reportedly have developed a deep learning model that can visually identify and analyze bacterial species in culture plates with a high level of agreement with interpretations made by medical technologists.
They initially trained and tested the system to digitally identify pathogens associated with urinary tract infections (UTIs). UTIs are the source for a large volume of clinical laboratory microbiological testing.
The system, known as DeepColony, uses hierarchical artificial intelligence technology. The researchers say hierarchical AI is better suited to complex decision-making than other approaches, such as generative AI.
In their Nature paper, the researchers explained that microbiologists use conventional methods to visually examine culture plates that contain bacterial colonies. The scientists hypothesize which species of bacteria are present, after which they test their hypothesis “by regrowing samples from each colony separately and then employing mass spectroscopy techniques,” to confirm their hypotheses.
However, DeepColony—which was designed for use with clinical laboratory automation systems—looks at high-resolution digital scans of cultured plates and attempts to identify the bacterial strains and analyze them in much the same way a microbiologist would. For example, it can identify species based on their appearance and determine which colonies are suitable for analysis, the researchers explained.
“Working on a large stream of clinical data, and a complete set of 32 pathogens, the proposed system is capable of effectively assisting plate interpretation with a surprising degree of accuracy in the widespread and demanding framework of urinary tract infections,” the study authors wrote. “Moreover, thanks to the rich species-related generated information, DeepColony can be used for developing trustworthy clinical decision support services in laboratory automation ecosystems from local to global scale.”
“Compared to the most common solutions based on single convolutional neural networks (CNN), multi-network architectures are attractive in our case because of their ability to fit into contexts where decision-making processes are stratified into a complex structure,” wrote the study’s lead author Alberto Signoroni, PhD (above), Associate Professor of Computer Science, University of Brescia, and his researcher team in their Nature paper. “The system must be designed to generate useful and easily interpretable information and to support expert decisions according to safety-by-design and human-in-the-loop policies, aiming at achieving cost-effectiveness and skill-empowerment respectively.” Microbiologists and clinical laboratory managers will want to follow the further development of this technology. (Photo copyright: University of Brescia.)
How Hierarchical AI Works
Writing in LinkedIn, patent attorney and self-described technology expert David Cain, JD, of Hauptman Ham, LLP, explained that hierarchical AI systems “are structured in layers, each with its own distinct role yet interconnected in a way that forms a cohesive whole. These systems are significant because they mirror the complexity of human decision-making processes, incorporating multiple levels of analysis and action. This multi-tiered approach allows for nuanced problem-solving and decision-making, akin to a seasoned explorer deftly navigating through a multifaceted terrain.”
DeepColony, the researchers wrote, consists of multiple convolutional neural networks (CNNs) that exchange information and cooperate with one another. The system is structured into five levels—labeled 0 through 4—each handling a different part of the analysis:
At level 0, the system determines the number of bacterial colonies and their locations on the plate.
At level 1, the system identifies “good colonies,” meaning those suitable for further identification and analysis.
At level 2, the system assigns each good colony to a bacterial species “based on visual appearance and growth characteristics,” the researchers wrote, referring to the determination as being “pathogen aware, similarity agnostic.”
The CNN used at this stage was trained by using images of 26,213 isolated colonies comprising 32 bacterial species, the researchers wrote in their paper. Most came from clinical laboratories, but some were obtained from the American Type Culture Collection (ATCC), a repository of biological materials and information resources available to researchers.
At level 3, the system attempts to improve accuracy by looking at the larger context of the plate. The goal here is to “determine if observed colonies are similar (pure culture) or different (mixed cultures),” the researchers wrote, describing this step as “similarity aware, pathogen agnostic.” This enables the system to recognize variants of the same strain, the researchers noted, and has the effect of reducing the number of strains identified by the system.
At this level, the system uses two “Siamese CNNs,” which were trained with a dataset of 200,000 image pairs.
Then, at level 4, the system “assesses the clinical significance of the entire plate,” the researchers added. Each plate is labeled as:
“Positive” (significant bacterial growth),
“No significant growth” (negative), or
“Contaminated,” meaning it has three or more “different colony morphologies without a particular pathogen that is prevalent over the others,” the researchers wrote.
If a plate is labeled as “positive,” it can be “further evaluated for possible downstream steps,” using MALDI-TOF mass spectrometry or tests to determine susceptibility to antimicrobial measures, the researchers stated.
“This decision-making process takes into account not only the identification results but also adheres to the specific laboratory guidelines to ensure a proper supportive interpretation in the context of use,” the researchers wrote.
Nearly 100% Agreement with Medical Technologists
To gauge DeepColony’s accuracy, the researchers tested it on a dataset of more than 5,000 urine cultures from a US laboratory. They then compared its analyses with those of human medical technologists who had analyzed the same samples.
Agreement was 99.2% for no-growth cultures, 95.6% for positive cultures, and 77.1% for contaminated or mixed growth cultures, the researchers wrote.
The lower agreement for contaminated cultures was due to “a deliberately precautionary behavior, which is related to ‘safety by design’ criteria,” the researchers noted.
Lead study author Alberto Signoroni, PhD, Associate Professor of Computer Science, University of Brescia, wrote in Nature that many of the plates identified by medical technologists as “contaminated” were labeled as “positive” by DeepColony. “We maximized true negatives while allowing for some false positives, so that DeepColony [can] focus on the most relevant or critical cases,” he said.
Will DeepColony replace medical technologists in clinical laboratories any time soon? Not likely. But the Brescia University study indicates the direction AI in healthcare is headed, with high accuracy and increasing speed. The day may not be far off when pathologists and microbiologists regularly employ AI algorithms to diagnose disease.
Another study in the United Kingdom that also used genomic analysis to understand drug-resistant Shigella produced findings that may be useful for microbiologists and medical laboratory scientists
From the onset of an infectious disease outbreak, public health officials, microbiologists, and clinical laboratory managers find it valuable to trace the origin of the spread back to the “index case” or “patient zero”—the first documented patient in the disease epidemic. Given the decreased cost of genomic analysis and improved accuracy of gene sequencing, infectious disease researchers are finding that task easier and faster than ever.
One recent example is a genomic study conducted at University of Washington (UW) in Seattle that enabled researchers to “retrace” the origin and spread of a “multidrug-resistant Shigellosis outbreak” from 2017 to 2022. “The aim of the study was to better understand the community transmission of Shigella and spread of antimicrobial resistance in our population, and to treat these multi-drug resistant infections more effectively,” the UW scientists stated in a new release.
Shigellosis (aka, bacillary dysentery) is a highly contagious disease of the intestines that can lead to hospitalization. Symptoms include fever, stomach cramps, diarrhea, dysentery, and dehydration.
“Additional analysis of the gut pathogen and its transmission patterns helped direct approaches to testing, treatment, and public health responses,” the UW news release states.
Usually prevalent in countries with public health and sanitation limitations, the “opportunistic” Shigella pathogen is now being seen in high-income countries as well, UW reported.
“You can’t really expect an infectious disease to remain confined to a specific at-risk population. [Shigella infections are] very much an emerging threat and something where our public health tools and therapeutic tools have significant limitations,” infectious disease specialist Ferric Fang, MD (above) told CIDRAP News. Fang is a UW professor of Microbiology and Clinical Laboratory Medicine and a corresponding author of the UW study. (Photo copyright: University of Washington.)
Generally, Shigella infects children, travelers, and men who have sex with men (MSM), the CDC noted.
The UW researchers were motivated to study Shigella when they noticed an uptick in drug-resistant shigellosis cases in Seattle’s homeless population in 2020 at the beginning of the COVID-19 pandemic, Center for Infectious Disease Research and Policy News (CIDRAP News) reported.
“Especially during the pandemic, a lot of public facilities were closed that homeless people were used to using,” infectious disease specialist Ferric Fang, MD, told CIDRAP News. Fang is Professor of Microbiology and Laboratory Medicine at University of Washington and corresponding author of the UW study.
The researchers studied 171 cases of Shigella identified from 2017 to 2022 by clinical laboratories at Harborview Medical Center and UW Medical Center in Seattle. According to CIDRAP News, the UW researchers found that:
46% were men who have sex with men (MSM).
51% were people experiencing homelessness (PEH).
Fifty-six patients were admitted to the hospital, with eight to an intensive care unit.
51% of isolates were multi-drug resistant (MDR).
Whole-Genome Sequencing Reveals Origin
The UW scientists characterized the stool samples of Shigella isolates by species identification, phenotypic susceptibility testing, and whole-genome sequencing, according to their Lancet Infectious Diseases paper. The paper also noted that 143 patients received antimicrobial therapy, and 70% of them benefited from the treatment for the Shigella infection.
Whole-genome sequencing revealed that two strains of Shigella (S. flexneri and S. sonnei) appeared first in Seattle’s MSM population before infecting the PEM population.
The genomic analysis found the outbreak of drug-resistant Shigella had international links as well, according to CIDRAP News:
One S. flexneri isolate was associated with a multi-drug resistant (MDR) strain from China, and
S. sonnei isolates resembled a strain characteristic of a current outbreak of MDR Shigella in England.
“The most prevalent lineage in Seattle was probably introduced to Washington State via international travel, with subsequent domestic transmission between at-risk groups,” the researchers wrote.
“Genomic analysis elucidated not only outbreak origin, but directed optimal approaches to testing, treatment, and public health response. Rapid diagnostics combined with detailed knowledge of local epidemiology can enable high rates of appropriate empirical therapy even in multidrug-resistant infection,” they continued.
UK Shigella Study Also Uses Genomics
Another study based in the United Kingdom (UK) used genomic analysis to investigate a Shigella outbreak as well.
Motivated by a UK Health Security Agency report of an increase in drug-resistance to common strains since 2021, the UK researchers studied Shigella cases from September 2015 to June 2022.
According to a paper they published in Lancet Infectious Diseases, the UK researchers “reported an increase in cases of sexually transmitted S. flexneri harboring blaCTX-M-27 (an antibiotic-resistant gene) in England, which is known to confer resistance to third-generation cephalosporins (antibiotics),” the researchers wrote.
Their analysis of plasmids (DNA with genes having antibiotic resistance) revealed a link in two drug-resistant Shigella strains at the same time, CIDRAP News explained.
“Our study reveals a worsening outlook regarding antimicrobial-resistant Shigella strains among MSM and highlights the value of continued integration of genomic analysis into surveillance and research,” the UK-based scientists wrote.
Current challenges associated with Shigella, especially as it evades treatment, may continue to demand attention from microbiologists, clinical laboratory scientists, and infectious disease specialists. Fortunately, use of genomic analysis—due to its ongoing improvements that have lowered cost and improved accuracy—has made it possible for public health researchers to better track the origins of disease outbreak and spread.
Illumina-bioMérieux Service to Aid Hospital and Public Health Labs
Illumina designated sequencing laboratories with Illumina MiSeq® systems will collaborate with microbiologists working in hospital and public health laboratories to prevent, rapidly track, and contain infectious disease agents in hospitals and communities. (more…)
Researchers say Mobidiag’s microarray-based diagnostic test technology looks promising
There’s a new DNA-based microarray platform that could speed identification of blood-borne pathogens. By allowing clinical laboratories to deliver test results in just 18 hours, use of this new microarray could improve early detection and management of sepsis patients.
In a study headed by Päivi Tissari, M.D., of the Division of Clinical Microbiology, Helsinki University Hospital Laboratory in Finland, the Prove-it sepsis assay, manufactured by Helsinki-based Mobidiag, demonstrated 94.7% clinical sensitivity, 98.8% specificity, along with 100% sensitivity and specificity for methicillin-resistant Staphylococcus aureus bacteremia. The conventional process of growing a culture—the medical laboratory’s gold standard—typically takes between one to three days to become positive and two more days to identify the bacteria and their antibiotic sensitivity patterns. Mobidiag’s Prove-it sepsis assay returns results in only 18 hours.
